The metric used to access the performance of the classification model is Confusion Metric. Confusion Metric can be further interpreted with the following terms:-

**True Positives (TP)** – These are the correctly predicted positive values. It implies that the value of the actual class is yes and the value of the predicted class is also yes.

**True Negatives (TN)** – These are the correctly predicted negative values. It implies that the value of the actual class is no and the value of the predicted class is also no.

**False positives and false negatives** , these values occur when your actual class contradicts with the predicted class.

**Now,**

**Recall,** also known as Sensitivity is the ratio of true positive rate (TP), to all observations in actual class – yes

Recall = TP/(TP+FN)

**Precision** is the ratio of positive predictive value, which measures the amount of accurate positives model predicted viz a viz number of positives it claims.

Precision = TP/(TP+FP)

**Accuracy** is the most intuitive performance measure and it is simply a ratio of correctly predicted observation to the total observations.

Accuracy = (TP+TN)/(TP+FP+FN+TN)

**F1 Score** is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account. Intuitively it is not as easy to understand as accuracy, but F1 is usually more useful than accuracy, especially if you have an uneven class distribution. Accuracy works best if false positives and false negatives have a similar cost. If the cost of false positives and false negatives are very different, it’s better to look at both Precision and Recall.